The shipping industry plays a vital role in global trade and also exerts significant environmental and social pressures due to high fuel consumption, greenhouse gas (GHG) emissions, and intensive port activities. Implementing environmental, social, and governance (ESG) practices offers a strategic pathway to enhance sustainability performance in this sector; however, it remains difficult to determine which managerial and operational actions can most effectively drive improvements in ESG performance. To address this challenge, this study proposes a novel framework that integrates a large language model (LLM), an extended q-rung orthopair fuzzy rough set (q-ROFRS)-based Delphi-weighted influence nonlinear gauge system (WINGS) model, and a fuzzy cognitive mapping (FCM) simulation. This approach systematically identifies critical success factors (CSFs), analyzes their cause-effect relationships, and simulates their dynamic influence on ESG performance in the shipping industry. The findings reveal that six CSFs, including structured board oversight, global policy engagement, comprehensive legal compliance framework, employee-centric culture, effective communication strategy, and integrated water resource management, serve as key causal drivers within the ESG system. FCM-based scenario simulations suggest that full activation of these modeled drivers could substantially improve projected ESG performance under the assumed scenario conditions. This research offers a dual contribution: Theoretically, it extends fuzzy decision-making and AI-driven causal modeling to complex sustainability systems; practically, it provides actionable guidance for shipping companies and policymakers to strengthen ESG integration, meet the International Maritime Organization's net-zero targets, and advance the United Nations Sustainable Development Goals (SDGs).
Environmental, Social, and Governance (ESG) controversies increasingly influence investment decisions, corporate accountability, and sustainability regulation. However, existing commercial controversy measures often rely on proprietary methodologies that limit transparency and make it difficult to understand how controversy assessments are generated. This paper aims to develop an ESG Controversy Index (ESGCI) that combines internationally recognised ESG principles with explainable artificial intelligence to provide a systematic approach to measuring firm-level ESG controversy exposure. The ESGCI is constructed from more than 5000 annual and sustainability reports issued by S&P 500 firms between 2010 and 2023, together with ESG doctrines published by the United Nations, the Principles for Responsible Investment, the Organisation for Economic Co-operation and Development and the International Labour Organisation. Natural language processing and supervised machine-learning techniques are used to identify, classify and aggregate ESG controversies within a structured measurement framework. The proposed index is evaluated through a multi-layer validation strategy incorporating conceptual assessment, document-level out-of-sample testing, external comparison with Refinitiv ESG controversy measures, inter-rater reliability, incremental validity and complementary robustness analyses. The findings show that the ESGCI captures controversy-related information recognised by existing commercial measures while providing additional insights through a fully documented measurement process. The reported relationships remain consistent across alternative weighting schemes, lagged specifications and instrumental-variable estimations, supporting the robustness of the empirical evidence. Rather than replacing existing ESG controversy ratings, the ESGCI offers a complementary approach that improves methodological transparency and facilitates reproducible ESG controversy measurement. By integrating institutional ESG principles with explainable AI and comprehensive validation procedures, this research seeks to contribute to the advancement of ESG measurement and provide a practical foundation for future research, investment analysis and sustainability reporting.
How does the relevance of ESG issues relate to firm performance, and does ESG relevance moderate the relationship between ESG engagement and firm financial outcomes? Using a novel indicator introduced by Fitch Ratings, we provide new empirical evidence to answer these questions. The forward-looking indicator measures the relevance of ESG issues to firms' credit ratings. It captures firms' financial sensitivity to ESG-related issues from a credit-rating perspective. Utilising a sample of 663 firms from 55 countries, we examine the relationship between ESG relevance, its subcategories and firm performance. We find that environmental relevance has a significant negative association with firm performance. The results are economically meaningful as a one-standard-deviation increase in the environmental relevance score associated with decreases in Tobin's Q and return on assets (ROA) by 5.7% and 11.9%, respectively. Furthermore, we investigate how ESG relevance interacts with ESG performance in explaining firm performance. We find that environmental relevance significantly weakens the positive association between environmental performance and firm financial performance. The association between ESG relevance and firm performance is primarily concentrated in environmental relevance, while the moderating effect is concentrated in Tobin's Q. The findings remain broadly robust to instrumental variable regressions, propensity score matching and alternative measures of firm performance. Further analysis of the disaggregated components of environmental relevance indicates that the negative association is primarily concentrated in the relevance of greenhouse gas emissions. Our findings provide important insights into why ESG relevance matters for investors and firm performance, an issue that is often neglected in the literature.
Retail investors evaluating impact-oriented opportunities must weigh economic, social, and environmental performance simultaneously, yet it remains unclear why they prioritize these dimensions so differently. We propose that personal values act as a weighting mechanism in multiattribute investment decisions. In a discrete choice experiment with 547 US retail investors, financial performance and risk dominate on average, but altruistic, biospheric, and egoistic values are each associated with greater weight on the corresponding performance dimension; attribute salience also varies across investment domains. Personal values thus help explain preference heterogeneity that demographic characteristics and cognitive biases alone leave unaccounted for.
This paper examines the effect of digitalisation on carbon intensity in 44 countries from 2000 to 2024 using a principal component index that combines internet use, mobile connections and landline telephone ownership. The standard model uses a static Common Correlated Effects Mean Group (CCEMG) regression. Other specifications, such as CS-ARDL(1,0), interaction, quadratic, interactive fixed-effects CCEMG threshold and dynamic subgroup estimators, allow for cross-sectional dependence, adjustment dynamics and heterogeneity. The results show no statistically significant average, short-run or long-run effect of digitalisation on carbon intensity. The interaction terms of financial development and institutional quality, and the squared digitalisation index, are also not statistically significant. Although the threshold model estimates a digitalisation level of 1.438, the likelihood-ratio confidence interval covers the search range, suggesting weak identification. Furthermore, digitalisation does not significantly affect carbon intensity in most country subgroups; the only exception is a long-run positive and marginally significant effect in countries with high-energy intensity. Energy intensity stands out as the most significant positive contributor to carbon intensity. Hence, the results refute the theoretical expectations of Ecological Modernisation and Socio-Technical Transition about the inherent environmental benefits of digital growth. Digital connectivity is an enabling infrastructure, with environmental impacts conditional on electricity supply and use. Given policy and firm practices, investments in digital connectivity should go hand in hand with investments in clean electricity, energy efficiency improvements and industry decarbonisation to advance SDGs 9, 12 and 13.
